Human Pose Estimation with Implicit Shape Models

This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions

Don't miss the Mismatch: Investigating the Objective Function Mismatch for Unsupervised Representation Learning

Finding general evaluation metrics for unsupervised representation learning techniques is a challenging open research question, which recently has become more and more necessary due to the increasing interest in unsupervised methods. Even though these methods promise beneficial representation characteristics, most approaches currently suffer from the objective function mismatch. This mismatch states that the performance on a desired target task can decrease when the unsupervised pretext task is learned too long - especially when both tasks are ill-posed. In this work, we build upon the widely used linear evaluation protocol and define new general evaluation metrics to quantitatively capture the objective function mismatch and the more generic metrics mismatch. We discuss the usability and stability of our protocols on a variety of pretext and target tasks and study mismatches in a wide range of experiments. Thereby we disclose dependencies of the objective function mismatch across several pretext and target tasks with respect to the pretext model's representation size, target model complexity, pretext and target augmentations as well as pretext and target task types.Comment: 21 pages, 17 figure

CSNNs: Unsupervised, Backpropagation-free Convolutional Neural Networks for Representation Learning

This work combines Convolutional Neural Networks (CNNs), clustering via Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks of Convolutional Self-Organizing Neural Networks (CSNNs), which learn representations in an unsupervised and Backpropagation-free manner. Our approach replaces the learning of traditional convolutional layers from CNNs with the competitive learning procedure of SOMs and simultaneously learns local masks between those layers with separate Hebbian-like learning rules to overcome the problem of disentangling factors of variation when filters are learned through clustering. We investigate the learned representation by designing two simple models with our building blocks, achieving comparable performance to many methods which use Backpropagation, while we reach comparable performance on Cifar10 and give baseline performances on Cifar100, Tiny ImageNet and a small subset of ImageNet for Backpropagation-free methods.Comment: 18 pages,18 figures, Author's extended version of the paper. Final version presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA). Boca Raton, Florida / USA. 201

Human Pose Estimation with Implicit Shape Models

Diese Doktorarbeit stellt einen neuen Ansatz vor, wie 3D Posen von Personen alleine auf Basis monokularer Bildinformation geschätzt werden können. Hierzu wird das Implicit Shape Modell um neue Votingstrategien erweitert, die die Lokalisierung anatomischer Landmarken im 2D Bildraum erlauben. Das anschließende eigentliche 3D Posenschätzungsproblem wird dann im Rahmen einer Partikel-Schwarm-Optimierung auf Basis der generierten Voteverteilungen formuliert

Human Pose Estimation with Implicit Shape Models

Diese Doktorarbeit stellt einen neuen Ansatz vor, wie 3D Posen von Personen alleine auf Basis monokularer Bildinformation geschätzt werden können. Hierzu wird das Implicit Shape Modell um neue Votingstrategien erweitert, die die Lokalisierung anatomischer Landmarken im 2D Bildraum erlauben. Das anschließende eigentliche 3D Posenschätzungsproblem wird dann im Rahmen einer Partikel-Schwarm-Optimierung auf Basis der generierten Voteverteilungen formuliert

How do US state firearms laws affect firearms manufacturing location? An empirical investigation, 1986-2010

We exploit variations in US state firearms laws to study their relation to the spatial distribution of more than 2700 federally licensed manufacturers of firearms for the civilian and law enforcement markets across the country. Accounting for a variety of economic factors¿such as cost, tax burden and agglomeration effects¿we find that states with relatively permissive, end-user friendly laws host more firearms manufacturing establishments than do states with relatively restrictive, end-user unfriendly laws. This supply side-oriented paper complements a literature that predominantly attends to the market's demand side. It thus opens up a new avenue to study the US civilian firearms market

Modeling the U.S. firearms market: the effects of civilian stocks, crime, legislation, and armed conflicte

This study represents an attempt to understand the U.S. firearms market – the largest in the world – in economic terms. A model of the underlying interplay of legal firearms supply and demand is a prerequisite for reliably evaluating the effectiveness of pertinent existing state and federal firearms policies, and to amend them as necessary. The stakes are high: compared to other nation-states, per capita firearms-related harm in the United States (including suicides and homicides) is exceptionally high and, within constitutional strictures, state and federal firearms policymakers increasingly view it as a major and pressing society-wide problem

Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation

A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level.Comment: 24 pages, 22 figures, under revie

The Physics of Protoplanetesimal Dust Agglomerates. III. Compaction in Multiple Collisions

To study the evolution of protoplanetary dust aggregates, we performed experiments with up to 2600 collisions between single, highly-porous dust aggregates and a solid plate. The dust aggregates consisted of spherical SiO$_2$ grains with 1.5$\mu$m diameter and had an initial volume filling factor (the volume fraction of material) of $\phi_0=0.15$. The aggregates were put onto a vibrating baseplate and, thus, performed multiple collisions with the plate at a mean velocity of 0.2 m s$^{-1}$. The dust aggregates were observed by a high-speed camera to measure their size which apparently decreased over time as a measure for their compaction. After 1000 collisions the volume filling factor was increased by a factor of two, while after $\sim2000$ collisions it converged to an equilibrium of $\phi\approx0.36$. In few experiments the aggregate fragmented, although the collision velocity was well below the canonical fragmentation threshold of $\sim1$ m s$^{-1}$. The compaction of the aggregate has an influence on the surface-to-mass ratio and thereby the dynamic behavior and relative velocities of dust aggregates in the protoplanetary nebula. Moreover, macroscopic material parameters, namely the tensile strength, shear strength, and compressive strength, are altered by the compaction of the aggregates, which has an influence on their further collisional behavior. The occurrence of fragmentation requires a reassessment of the fragmentation threshold velocity.Comment: accepted by the Astrophysical Journa

Newtonian Cosmology in Lagrangian Formulation: Foundations and Perturbation Theory

The ``Newtonian'' theory of spatially unbounded, self--gravitating, pressureless continua in Lagrangian form is reconsidered. Following a review of the pertinent kinematics, we present alternative formulations of the Lagrangian evolution equations and establish conditions for the equivalence of the Lagrangian and Eulerian representations. We then distinguish open models based on Euclidean space $\R^3$ from closed models based (without loss of generality) on a flat torus \T^3. Using a simple averaging method we show that the spatially averaged variables of an inhomogeneous toroidal model form a spatially homogeneous ``background'' model and that the averages of open models, if they exist at all, in general do not obey the dynamical laws of homogeneous models. We then specialize to those inhomogeneous toroidal models whose (unique) backgrounds have a Hubble flow, and derive Lagrangian evolution equations which govern the (conformally rescaled) displacement of the inhomogeneous flow with respect to its homogeneous background. Finally, we set up an iteration scheme and prove that the resulting equations have unique solutions at any order for given initial data, while for open models there exist infinitely many different solutions for given data.Comment: submitted to G.R.G., TeX 30 pages; AEI preprint 01